Skip to main content

Robust and Efficient Multi-objective Automatic Adjustment for Optical Axes in Laser Systems Using Stochastic Binary Search Algorithm

  • Conference paper
Evolvable Systems: From Biology to Hardware (ICES 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4684))

Included in the following conference series:

Abstract

The adjustment of optical axes is crucial for laser systems. We have previously proposed an automatic adjustment method using genetic algorithms to adjust the optical axes. However, there were still two problems that needed to be solved: (1)long adjustment times, and (2)adjustment precision due to observation noise. In order to solve these tasks, we propose a robust and efficient automatic multi-objective adjustment method using stochastic binary search algorithm. Adjustment experiments for optical axes with 4-DOF in noisy environment demonstrate that the proposed method can robustly adjust the positioning and the angle of the optical axes in about 12 minutes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Murakawa, M., Itatani, T., Kasai, Y., Yoshikawa, H., Higuchi, T.: An evolvable laser system for generating femtosecond pulses. In: GECCO 2000. Proceedings of the Second Genetic and Evolutionary Computation Conference, pp. 636–642 (2000)

    Google Scholar 

  2. Nosato, H., Kasai, Y., Murakawa, M., Itatani, T., Higuchi, T.: Automatic adjustments of a femtosecond-pulses laser using genetic algorithms. In: CEC 2003. Proceedings of 2003 Congress on Evolutionary Computation, pp. 2096–2101 (2003)

    Google Scholar 

  3. Murata, N., Nosato, H., Furuya, T., Murakawa, M.: An automatic multi-objective adjustment system for optical axes using genetic algorithms. In: ISDA 2005. Proceedings of 5th International Conference on Intelligent Systems Design and Applications, pp. 546–551 (2005)

    Google Scholar 

  4. Murata, N., Nosato, H., Furuya, T., Murakawa, M.: Robust and efficient automatic adjustment for optical axes in laser systems using binary search algorithm for noisy environments. In: ICARA 2006. Proceedings of The 3rd International Conference on Autonomous Robots and Agents, pp. 261–266 (2006)

    Google Scholar 

  5. Hughes, E.J.: Multi-objective binary search optimisation. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 102–117. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  6. Fitzpatrick, J.M., Greffenstette, J.J.: Genetic algorithms in noisy environments. Machine Learning 3, 101–120 (1988)

    Google Scholar 

  7. Stagge, P.: Averaging efficiently in the presence of noise. In: PPSN V. Proceedings of Parallel Problem Solving from Nature, pp. 188–197 (1998)

    Google Scholar 

  8. Watanabe, S., Hiroyasu, T., Miki, M.: Neighborhood Cultivation Genetic Algorithm for Multi-Objective Optimization Problems. In: SEAL 2002. Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, vol. 1, pp. 198–202 (2002)

    Google Scholar 

  9. Knowles, J., Thiele, L., Zitzler, E.: A tutorial oh the performance assessment of stochastic multiobjective optimizers. Report of Computer Engineering and Networks Laboratory (TIK) (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Lishan Kang Yong Liu Sanyou Zeng

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Murata, N., Nosato, H., Furuya, T., Murakawa, M. (2007). Robust and Efficient Multi-objective Automatic Adjustment for Optical Axes in Laser Systems Using Stochastic Binary Search Algorithm. In: Kang, L., Liu, Y., Zeng, S. (eds) Evolvable Systems: From Biology to Hardware. ICES 2007. Lecture Notes in Computer Science, vol 4684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74626-3_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74626-3_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74625-6

  • Online ISBN: 978-3-540-74626-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics